"""BrowserGym MiniWoB example with Qwen deciding the next action.

This is an inference example for the BrowserGym environment. It uses the OpenAI
client and a vision language model to decide the next action. We use Hugging Face
Inference Providers API to access the model, but you can use any other provider that
is compatible with the OpenAI API.

Prerequisites:
- (Optional) Export the MiniWoB URL if you are hosting the tasks yourself
  (must include the `/miniwob/` suffix); the BrowserGym Docker image now
  serves the MiniWoB bundle internally on port 8888.
- Export your Hugging Face token for the router:
    `export HF_TOKEN=your_token_here`

Usage:
    python examples/browsergym_example.py
"""

import os
import re
import base64
import textwrap
from io import BytesIO
from typing import List, Optional, Dict

from openai import OpenAI
import numpy as np
from PIL import Image

from envs.browsergym_env import BrowserGymAction, BrowserGymEnv

API_BASE_URL = "https://router.huggingface.co/v1"
API_KEY = os.getenv("HF_TOKEN") or os.getenv("API_KEY")
MODEL_NAME = "Qwen/Qwen3-VL-30B-A3B-Instruct:novita"
MAX_STEPS = 8
MAX_DOM_CHARS = 3500
TEMPERATURE = 0.2
MAX_TOKENS = 200
FALLBACK_ACTION = "noop()"

DEBUG = True
ACTION_PREFIX_RE = re.compile(
    r"^(action|next action)\s*[:\-]\s*",
    re.IGNORECASE,
)
ACTION_PATTERN = re.compile(r"[A-Za-z_]+\s*\(.*\)", re.DOTALL)


SYSTEM_PROMPT = textwrap.dedent(
    """
    You control a web browser through BrowserGym.
    Reply with exactly one action string.
    The action must be a valid BrowserGym command such as:
    - noop()
    - click('<BID>')
    - type('selector', 'text to enter')
    - fill('selector', 'text to enter')
    - send_keys('Enter')
    - scroll('down')
    Use single quotes around string arguments.
    When clicking, use the BrowserGym element IDs (BIDs) listed in the user message.
    If you are unsure, respond with noop().
    Do not include explanations or additional text.
    """
).strip()


def build_history_lines(history: List[str]) -> str:
    if not history:
        return "None"
    return "\n".join(history[-4:])


def extract_screenshot_uri(observation) -> Optional[str]:
    if observation.screenshot is None:
        return None
    screen_array = np.array(observation.screenshot, dtype=np.uint8)
    image = Image.fromarray(screen_array)
    buffer = BytesIO()
    image.save(buffer, format="PNG")
    buffer.seek(0)
    data_uri = base64.b64encode(buffer.read()).decode("utf-8")
    return f"data:image/png;base64,{data_uri}"


def extract_clickable_elements(observation) -> List[Dict[str, str]]:
    """Collect BrowserGym element IDs that can be clicked."""

    metadata = getattr(observation, "metadata", {}) or {}
    obs_dict = metadata.get("browsergym_obs", {}) or {}
    extra_props = obs_dict.get("extra_element_properties", {}) or {}

    clickables: List[Dict[str, str]] = []
    for bid, props in extra_props.items():
        if not props.get("clickable"):
            continue

        bbox = props.get("bbox") or []
        bbox_str = ", ".join(bbox) if bbox else "?"
        clickables.append(
            {
                "bid": str(bid),
                "bbox": bbox_str,
            }
        )

    # Keep a stable ordering for readability
    clickables.sort(key=lambda item: item["bid"])
    return clickables


def build_user_prompt(step: int, observation, history: List[str]) -> str:
    goal = observation.goal or "(not provided)"
    url = observation.url or "(unknown)"
    error_note = "Yes" if observation.last_action_error else "No"

    clickables = extract_clickable_elements(observation)
    if clickables:
        actions_hint = "\n".join(
            f"    - {item['bid']} (bbox: {item['bbox']})" for item in clickables
        )
    else:
        actions_hint = "    (none detected)"

    prompt = textwrap.dedent(
        f"""
        Step: {step}
        Goal: {goal}
        Current URL: {url}
        Previous steps:
        {build_history_lines(history)}
        Last action error: {error_note}

        Available clickable element IDs: {actions_hint}

        Reply with exactly one BrowserGym action string.
        """
    ).strip()
    return prompt


def parse_model_action(response_text: str) -> str:
    if not response_text:
        return FALLBACK_ACTION

    # Prefer the first line that looks like an action string
    lines = response_text.splitlines()
    for raw_line in lines:
        line = raw_line.strip()
        if not line:
            continue
        line = ACTION_PREFIX_RE.sub("", line)
        match = ACTION_PATTERN.search(line)
        if match:
            action = match.group(0).strip()
            # Collapse internal whitespace
            action = re.sub(r"\s+", " ", action)
            # If the model tried to click by natural-language description while we
            # only exposed numeric BrowserGym IDs, fallback to the single detected ID.
            return action

    # Fall back to searching the whole response
    match = ACTION_PATTERN.search(response_text)
    if match:
        action = match.group(0).strip()
        action = re.sub(r"\s+", " ", action)
        return action

    return FALLBACK_ACTION


def main() -> None:
    client = OpenAI(base_url=API_BASE_URL, api_key=API_KEY)

    env = BrowserGymEnv.from_docker_image(
        image="browsergym-env:latest",
        env_vars={
            "BROWSERGYM_BENCHMARK": "miniwob",
            "BROWSERGYM_TASK_NAME": "click-test",
        },
    )

    history: List[str] = []

    try:
        result = env.reset()
        observation = result.observation
        print(f"Episode goal: {observation.goal}")

        for step in range(1, MAX_STEPS + 1):
            if result.done:
                print("Environment signalled done. Stopping early.")
                break

            user_prompt = build_user_prompt(step, observation, history)
            user_content = [{"type": "text", "text": user_prompt}]
            screenshot_uri = extract_screenshot_uri(observation)
            if screenshot_uri:
                user_content.append(
                    {
                        "type": "image_url",
                        "image_url": {"url": screenshot_uri},
                    }
                )

            messages = [
                {
                    "role": "system",
                    "content": [{"type": "text", "text": SYSTEM_PROMPT}],
                },
                {
                    "role": "user",
                    "content": user_content,
                },
            ]

            try:
                completion = client.chat.completions.create(
                    model=MODEL_NAME,
                    messages=messages,
                    temperature=TEMPERATURE,
                    max_tokens=MAX_TOKENS,
                    stream=False,
                )
                response_text = completion.choices[0].message.content or ""
            # pylint: disable=broad-except
            except Exception as exc:  # noqa: BLE001
                failure_msg = f"Model request failed ({exc}). Using fallback action."
                print(failure_msg)
                response_text = FALLBACK_ACTION

            action_str = parse_model_action(response_text)
            print(f"Step {step}: model suggested -> {action_str}")

            result = env.step(BrowserGymAction(action_str=action_str))
            observation = result.observation

            reward = result.reward or 0.0
            error_flag = " ERROR" if observation.last_action_error else ""
            history_line = (
                f"Step {step}: {action_str} -> reward {reward:+.2f}{error_flag}"
            )
            history.append(history_line)
            print(
                "  Reward: "
                f"{reward:+.2f} | Done: {result.done} | Last action error: "
                f"{observation.last_action_error}"
            )

            if result.done:
                print("Episode complete.")
                break

        else:
            print(f"Reached max steps ({MAX_STEPS}).")

    finally:
        env.close()


if __name__ == "__main__":
    main()
